Database Performance Tuning

Finding and fixing what makes database queries slow: query plans, indexes, configuration, locking, bloat, connection limits and hardware. The standard approach is to measure, find the heaviest queries, analyze their plans, change one thing and measure again.

Use the database's built-in statistics: pg_stat_statements for PostgreSQL, Query Store for SQL Server and Azure SQL, the Performance Schema for MySQL, or a cloud tool such as Performance Insights or Query Insights. Monitoring platforms such as pganalyze or Percona Monitoring and Management aggregate these over time.

It shows the execution plan the optimizer chose for a query: scans, joins, row estimates and costs. EXPLAIN ANALYZE also runs the query and reports actual timings. Tools such as PEV2 and pgMustard visualize the plan.

Only as many as queries need. Indexes speed up reads but slow writes and use storage, so check usage statistics and remove unused ones. HypoPG lets you test hypothetical indexes in PostgreSQL before creating them.

Automated tuners can suggest configuration or index changes, but results depend on workload. OtterTune, a well-known research-origin service, now describes itself as discontinued. Test any automated change on a copy of production data first.

Each database connection uses memory and CPU, so thousands of application connections can slow a server. Poolers such as PgBouncer and ProxySQL share a smaller set of connections across clients.

Links and release facts were checked on 31 October 2026 from GitHub. The MySQL documentation returned 403 to automated checks. Several benchmark tools, including sysbench and YCSB, have not had a release in years, as their cards note. Vendor descriptions follow their own pages and are not rankings.